Business intelligence and data analytics are similar at first glance but have two starkly different purposes in the enterprise. BI refers to systems and tools that analyze corporate data for decision-making, while data analytics refers to the processing of raw data into a digestible format.
Despite these differences, both can work together to fuel business growth and increase data value.
There are three main differences between BI and data analytics to be aware of. Understanding these differences will allow you to apply them against business objectives appropriately.
Order of Implementation – Data analytics will need to be implemented first to allow for business intelligence generation. This will involve the installation of data storage infrastructure, software to perform data cleansing processes, and standardization tools to facilitate cross-software data compatibility. The goal of data analytics is to convert raw datasets into a structured format that can be easily interpreted across the business.
Business intelligence will only be possible after these structured datasets are ready. BI aims to assess overall progress against business objectives, so you can make the necessary strategic changes to achieve your goals.
System Management – With business intelligence, errors in reporting can be attributed to incorrect query usage or using the wrong data source for that particular report. To overcome these issues, you would need to change your reference source or modify the query parameters to pull more relevant data.
When troubleshooting data analytics, any reporting errors will be due to your chosen data model. If your unstructured data is being formatted in a way that doesn’t meet business requirements, you would need to modify the data model to meet demands. This is an ongoing process, as business requirements may well change over time, necessitating data model augmentation.
The Purpose – Business intelligence is commonly used to convey actionable information to internal stakeholders. Through historical trend analysis, BI reports and dashboards can be generated to guide intradepartmental change. This means that business intelligence offers retroactive rather than proactive insight.
In contrast, data analytics aims to prepare unstructured datasets for quantitative analysis. The real-time nature of data analytics allows for instant decision-making and prediction of future trends, offering proactive rather than retroactive business insight.
Business intelligence and data analytics can both work in tandem to drive business growth. By setting up an analytics framework that directly feeds your business intelligence software, you can start to promote more proactive decision-making across your business.
In the world of big data, businesses are amassing large quantities of unstructured information that could be used for business intelligence generation. With Talend’s Real-Time Big Data platform, you can incorporate features like Spark Streaming and Machine Learning to further increase your data potential.
Spark Streaming – This functionality is part of the Apache Spark unified analytics engine for big data. It enables scalable, high-throughput, fault-tolerant stream processing of live data. In short, Spark Streaming is a form of batch data processing that uses complex algorithms to automatically input information into your databases.
Talend offers full Spark Streaming integration on its platform, which allows for reliable, real-time business intelligence generation.
Machine Learning for Data Science – Talend incorporates machine learning into its Real-Time Big Data platform.
You can use classification algorithms to automate pattern detection in your datasets. Use cases include spam detection, image categorization, and sentiment analysis in end-user communications.
Talend also uses clustering algorithms, which is a form of exploratory data mining. This allows for statistical data analysis that can be used for pricing segmentation, customer loyalty determination, and fraud detection.
Trianz is a leading data integration consulting firm who has partnered with Talend to deliver industry-leading BI and data analytics solutions through their platform. The future of enterprise IT strategy will be formed around AI, which is why we work closely with you to prepare your infrastructure and employees for this incoming paradigm shift.
Contact Us Today
A proper data governance strategy has become more vital than ever, thanks to the ratification of data protection regulations like the GDPR and CCPA. Despite this consensus, the CIO WaterCooler Data Governance Survey revealed that 53% of companies had only just started working on their enterprise data governance strategy in 2017.Explore
The idea of data warehousing was first explored in detail back in 1988 by a team of researchers at IBM. In their whitepaper, Barry Devlin and Paul Murphy went into detail about the environments in which companies were maintaining their database instances. They highlighted the growing need at IBM for further integration to simplify access and improve data consistency for end-users. This was during a time where “big data” was unheard of, with businesses managing much smaller datasets.Explore
With the California Consumer Protection Act (CCPA) and General Data Protection Regulation (GDPR) now in full swing, the regulatory landscape for businesses has never been more complicated. Consumers are increasingly aware of cybersecurity in their daily lives, and they are demanding better protection against these threats when using online services.Explore
With businesses collecting massive quantities of information daily, it is crucial to maintain data accessibility for employees. The art of data integration involves combining datasets from different sources to create a unified focal point for analysis and insight generation. This achieves a state of data confluence, which can be beneficial in a variety of use cases including commercial applications and scientific research.Explore
As internet usage continues to grow, so does the generation of raw data. Businesses need to process this raw data into useable structured data, so they can extract insight to guide their enterprise strategy. Without a unified, integrated IT infrastructure, data processing will become more complicated due to growing service quality demands and an increased number of data sources.Explore
With the explosive growth of big data and cloud computing, we’ve seen a remarkable increase in the quantities of data we generate each day. According to a study by EMC Corporation, it is estimated that we will generate 1.7 megabytes of data every second, of every day, for every single person on the planet in 2020. That is equivalent to 40 Zettabytes, or 40 billion Terabytes per year. This is a goldmine of information, but even more surprising is the fact that only 12% of this data is ever properly analyzed—namely due to a lack of effective data governance.Explore